Overview

Dataset statistics

Number of variables12
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.5 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with invoice_quantity and 3 other fieldsHigh correlation
recency_days is highly overall correlated with invoice_quantityHigh correlation
invoice_quantity is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
items_quantity is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
products_quantity is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_unique_basket_size is highly overall correlated with products_quantity and 1 other fieldsHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.44422362)Skewed
returns is highly skewed (γ1 = 51.79774426)Skewed
frequency is highly skewed (γ1 = 24.88049136)Skewed
avg_basket_size is highly skewed (γ1 = 44.67271661)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.1%) zerosZeros
returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-01-04 12:40:06.501653
Analysis finished2023-01-04 12:40:28.167298
Duration21.67 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:28.261089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityNot monotonic
2023-01-04T09:40:28.408131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.3217
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:28.555527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.623
Coefficient of variation (CV)3.8484486
Kurtosis353.94472
Mean2749.3217
Median Absolute Deviation (MAD)672.16
Skewness16.777556
Sum8162736.2
Variance1.1194959 × 108
MonotonicityNot monotonic
2023-01-04T09:40:28.688703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.96 2
 
0.1%
533.33 2
 
0.1%
889.93 2
 
0.1%
2053.02 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
1353.74 2
 
0.1%
331 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.287639
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:28.838017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756779
Coefficient of variation (CV)1.2095137
Kurtosis2.7779627
Mean64.287639
Median Absolute Deviation (MAD)26
Skewness1.7983795
Sum190870
Variance6046.1167
MonotonicityNot monotonic
2023-01-04T09:40:28.986953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

invoice_quantity
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7231391
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:29.147779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8565313
Coefficient of variation (CV)1.5474954
Kurtosis190.83445
Mean5.7231391
Median Absolute Deviation (MAD)2
Skewness10.766805
Sum16992
Variance78.438147
MonotonicityNot monotonic
2023-01-04T09:40:29.292789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

items_quantity
Real number (ℝ)

Distinct1671
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.8525
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:29.456374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.4
Q1296
median641
Q31401
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1105

Descriptive statistics

Standard deviation5887.578
Coefficient of variation (CV)3.6594891
Kurtosis465.99808
Mean1608.8525
Median Absolute Deviation (MAD)422
Skewness17.858591
Sum4776683
Variance34663575
MonotonicityNot monotonic
2023-01-04T09:40:29.622494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
150 9
 
0.3%
88 9
 
0.3%
246 8
 
0.3%
272 8
 
0.3%
84 8
 
0.3%
260 8
 
0.3%
288 8
 
0.3%
1200 7
 
0.2%
516 7
 
0.2%
Other values (1661) 2886
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%

products_quantity
Real number (ℝ)

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.72415
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:29.790500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.89641
Coefficient of variation (CV)2.1992119
Kurtosis354.86113
Mean122.72415
Median Absolute Deviation (MAD)44
Skewness15.707635
Sum364368
Variance72844.071
MonotonicityNot monotonic
2023-01-04T09:40:29.949878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
35 35
 
1.2%
29 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 30
 
1.0%
Other values (458) 2629
88.5%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.897762
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:30.122124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9166611
Q113.119333
median17.956587
Q324.988286
95-th percentile90.497
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.868952

Descriptive statistics

Standard deviation1036.9344
Coefficient of variation (CV)19.98033
Kurtosis2890.7071
Mean51.897762
Median Absolute Deviation (MAD)5.984842
Skewness53.444224
Sum154084.45
Variance1075233
MonotonicityNot monotonic
2023-01-04T09:40:30.283502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2956) 2956
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.156955
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:30.450467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.4961
Coefficient of variation (CV)24.333498
Kurtosis2765.5286
Mean62.156955
Median Absolute Deviation (MAD)1
Skewness51.797744
Sum184544
Variance2287644.6
MonotonicityNot monotonic
2023-01-04T09:40:30.624726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (204) 706
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.348511
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:30.795892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.923077
median48.285714
Q385.333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.410256

Descriptive statistics

Standard deviation63.544929
Coefficient of variation (CV)0.94352388
Kurtosis4.8871091
Mean67.348511
Median Absolute Deviation (MAD)26.285714
Skewness2.0627709
Sum199957.73
Variance4037.958
MonotonicityNot monotonic
2023-01-04T09:40:31.220517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
46 17
 
0.6%
21 17
 
0.6%
11 17
 
0.6%
42 16
 
0.5%
Other values (1248) 2777
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1137973
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:31.391220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088941642
Q10.016339869
median0.025889968
Q30.049450549
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.03311068

Descriptive statistics

Standard deviation0.40815625
Coefficient of variation (CV)3.5866953
Kurtosis989.36508
Mean0.1137973
Median Absolute Deviation (MAD)0.012191338
Skewness24.880491
Sum337.8642
Variance0.16659153
MonotonicityNot monotonic
2023-01-04T09:40:31.557605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.03571428571 13
 
0.4%
0.07692307692 13
 
0.4%
Other values (1215) 2636
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct1005
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.154708
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:31.744366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.512322
Coefficient of variation (CV)0.88073027
Kurtosis27.703297
Mean22.154708
Median Absolute Deviation (MAD)8.2
Skewness3.4994559
Sum65777.329
Variance380.73071
MonotonicityNot monotonic
2023-01-04T09:40:31.892898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 53
 
1.8%
14 39
 
1.3%
11 38
 
1.3%
20 33
 
1.1%
9 33
 
1.1%
1 32
 
1.1%
17 31
 
1.0%
18 30
 
1.0%
10 30
 
1.0%
5 29
 
1.0%
Other values (995) 2621
88.3%
ValueCountFrequency (%)
1 32
1.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 8
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.81376
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-04T09:40:32.107283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.33333
Q3281.69231
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.44231

Descriptive statistics

Standard deviation791.55519
Coefficient of variation (CV)3.1685812
Kurtosis2255.5382
Mean249.81376
Median Absolute Deviation (MAD)83.083333
Skewness44.672717
Sum741697.07
Variance626559.62
MonotonicityNot monotonic
2023-01-04T09:40:32.267275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
60 8
 
0.3%
88 8
 
0.3%
75 8
 
0.3%
136 8
 
0.3%
208 7
 
0.2%
Other values (1969) 2882
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

Interactions

2023-01-04T09:40:26.055611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:06.848313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:08.646231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:10.278027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:11.948378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:13.654367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:15.362560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:17.079669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:18.897484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:20.796391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:22.506073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:24.419134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:26.189303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:06.991228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:08.777886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:10.410785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:12.068878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:13.796108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:15.493826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:17.204491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:19.037226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:20.937231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:22.652264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:24.547780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:26.317273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:07.119488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:08.906781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:10.548792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:12.188579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:13.933503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:15.627512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:17.333100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:19.186579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:21.076116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:22.788390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:24.677125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:26.459370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:07.257910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:09.053618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:10.691683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:12.324152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:14.071458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:15.767819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:17.469812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:19.328621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:21.217549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:22.939725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:24.811188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:26.600537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:07.386259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:09.183196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:10.822787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:12.439688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:14.211768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:15.896664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:17.587163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:19.471827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:21.340548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:23.071994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:24.929554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:26.750026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:07.524999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:09.330829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:10.959764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:12.580289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:14.358332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:16.051754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:17.912054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:19.637017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:21.490098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:23.213484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:25.071123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:26.898297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:07.674932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:09.476700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:11.101697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:12.865810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:14.504099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:16.202396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:18.072927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:19.852652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:21.637162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:23.368265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:25.219705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:27.028083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:07.804506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:09.611805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:11.230272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:12.988436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:14.637894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:16.343472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:18.204504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:20.008027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:21.772027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:23.497801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:25.348329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:27.174846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:07.953293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:09.754464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:11.373354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:13.125291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:14.786656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:16.488642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:18.348431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:20.160109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:21.915659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:23.649829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:25.485037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:27.321780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:08.093160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:09.883751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:11.509845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:13.257618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:14.926885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:16.635180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:18.489540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:20.315807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:22.061573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:23.785550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:25.626021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:27.460387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:08.240599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:10.019703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:11.661003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:13.382794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:15.072047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:16.784729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:18.627797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:20.469842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:22.205448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:23.920285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:25.767656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:27.600506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:08.367935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:10.146111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:11.797186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:13.512857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:15.206439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:16.929621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:18.755518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:20.639443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:22.354086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:24.061333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-04T09:40:25.903764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-04T09:40:32.415604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idgross_revenuerecency_daysinvoice_quantityitems_quantityproducts_quantityavg_ticketreturnsavg_recency_daysfrequencyavg_unique_basket_sizeavg_basket_size
customer_id1.000-0.0760.0010.026-0.0700.013-0.131-0.0630.019-0.002-0.007-0.123
gross_revenue-0.0761.000-0.4150.7700.9250.7440.2460.372-0.2470.0900.2910.574
recency_days0.001-0.4151.000-0.502-0.408-0.4350.048-0.1200.1080.018-0.106-0.098
invoice_quantity0.0260.770-0.5021.0000.7160.6900.0590.294-0.2590.0790.0250.100
items_quantity-0.0700.925-0.4080.7161.0000.7300.1670.344-0.2270.0800.3200.729
products_quantity0.0130.744-0.4350.6900.7301.000-0.3770.242-0.1660.0360.6990.383
avg_ticket-0.1310.2460.0480.0590.167-0.3771.0000.190-0.1220.090-0.6110.188
returns-0.0630.372-0.1200.2940.3440.2420.1901.000-0.3960.2340.0190.210
avg_recency_days0.019-0.2470.108-0.259-0.227-0.166-0.122-0.3961.000-0.8810.048-0.077
frequency-0.0020.0900.0180.0790.0800.0360.0900.234-0.8811.000-0.0720.027
avg_unique_basket_size-0.0070.291-0.1060.0250.3200.699-0.6110.0190.048-0.0721.0000.447
avg_basket_size-0.1230.574-0.0980.1000.7290.3830.1880.210-0.0770.0270.4471.000

Missing values

2023-01-04T09:40:27.811660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-04T09:40:28.053536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysinvoice_quantityitems_quantityproducts_quantityavg_ticketreturnsavg_recency_daysfrequencyavg_unique_basket_sizeavg_basket_size
0178505391.21372.034.01733.0297.018.15222240.035.50000017.0000008.73529450.970588
1130473232.5956.09.01390.0171.018.90403535.027.2500000.02830219.000000154.444444
2125836705.382.015.05028.0232.028.90250050.023.1875000.04032315.466667335.200000
313748948.2595.05.0439.028.033.8660710.092.6666670.0179215.60000087.800000
415100876.00333.03.080.03.0292.00000022.08.6000000.0731711.00000026.666667
5152914623.3025.014.02102.0102.045.32647129.023.2000000.0401157.285714150.142857
6146885630.877.021.03621.0327.017.219786399.018.3000000.05722115.571429172.428571
7178095411.9116.012.02057.061.088.71983641.035.7000000.0335205.083333171.416667
81531160767.900.091.038194.02379.025.543464474.04.1444440.24331626.142857419.714286
9160982005.6387.07.0613.067.029.9347760.047.6666670.0243909.57142987.571429
customer_idgross_revenuerecency_daysinvoice_quantityitems_quantityproducts_quantityavg_ticketreturnsavg_recency_daysfrequencyavg_unique_basket_sizeavg_basket_size
5627177271060.2515.01.0645.066.016.0643946.06.01.00000066.0645.000000
563717232421.522.02.0203.036.011.7088890.012.00.15384618.0101.500000
563817468137.0010.02.0116.05.027.4000000.04.00.4000002.558.000000
564913596697.045.02.0406.0166.04.1990360.07.00.25000083.0203.000000
5655148931237.859.02.0799.073.016.9568490.02.00.66666736.5399.500000
565912479473.2011.01.0382.030.015.77333334.04.01.00000030.0382.000000
568014126706.137.03.0508.015.047.07533350.03.00.7500005.0169.333333
5686135211092.391.03.0733.0435.02.5112410.04.50.300000145.0244.333333
569615060301.848.04.0262.0120.02.5153330.01.02.00000030.065.500000
571512558269.967.01.0196.011.024.541818196.06.01.00000011.0196.000000